This notebook contains a set of analyses for analyzing Jkvandelay’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
Jkvandelay | training | published before 2020 | 88 | 123 |
Jkvandelay | validation | published 2020 | 4 | 3 |
Jkvandelay | test | published after 2020 | 0 | 0 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
Jkvandelay | Artist Harald Lieske | 10.2% | 0.5% | 19.20 |
Jkvandelay | Artist Claus Stephan | 8.0% | 0.6% | 13.29 |
Jkvandelay | Days Of Wonder | 2.3% | 0.2% | 12.11 |
Jkvandelay | ZMan Games | 14.8% | 1.4% | 10.51 |
Jkvandelay | Asmodee | 25.0% | 2.5% | 9.84 |
Jkvandelay | Rio Grande Games | 18.2% | 1.9% | 9.74 |
Jkvandelay | City Building | 13.6% | 2.3% | 6.00 |
Jkvandelay | Map Continental National Scale | 11.4% | 1.9% | 5.89 |
Jkvandelay | Voting | 11.4% | 2.1% | 5.31 |
Jkvandelay | Artist Franz Vohwinkel | 6.8% | 1.5% | 4.46 |
Jkvandelay | GMT Games | 4.5% | 1.3% | 3.42 |
Jkvandelay | USA | 5.7% | 1.7% | 3.26 |
Jkvandelay | Deduction Game | 14.8% | 5.1% | 2.90 |
Jkvandelay | Solo Solitaire Game | 4.5% | 3.3% | 1.37 |
Jkvandelay | Dice Rolling | 17.0% | 28.4% | 0.60 |
Jkvandelay | Murder Mystery | 0.0% | 1.5% | 0.00 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2011 | 84876 | The Castles of Burgundy | 0.852 | yes |
2 | 2010 | 70512 | Luna | 0.850 | no |
3 | 2010 | 66505 | The Speicherstadt | 0.812 | no |
4 | 2014 | 159508 | AquaSphere | 0.808 | yes |
5 | 2013 | 137408 | Amerigo | 0.771 | yes |
6 | 2006 | 19948 | Rum & Pirates | 0.744 | no |
7 | 2007 | 31594 | In the Year of the Dragon | 0.732 | yes |
8 | 2009 | 55670 | Macao | 0.722 | no |
9 | 2007 | 25554 | Notre Dame | 0.682 | yes |
10 | 2008 | 38453 | Space Alert | 0.680 | yes |
11 | 2011 | 91873 | Strasbourg | 0.626 | no |
12 | 2008 | 35488 | The Name of the Rose | 0.568 | no |
13 | 2017 | 232988 | The Castles of Burgundy: The Dice Game | 0.558 | no |
14 | 2013 | 143693 | Glass Road | 0.556 | no |
15 | 2018 | 205896 | Rising Sun | 0.538 | no |
16 | 2019 | 285984 | Last Bastion | 0.536 | no |
17 | 2012 | 123260 | Suburbia | 0.534 | no |
18 | 2010 | 25292 | Merchants & Marauders | 0.501 | no |
19 | 2017 | 233078 | Twilight Imperium: Fourth Edition | 0.495 | no |
20 | 2016 | 205398 | Citadels | 0.474 | no |
21 | 2016 | 191977 | The Castles of Burgundy: The Card Game | 0.454 | no |
22 | 2013 | 136888 | Bruges | 0.452 | yes |
23 | 2016 | 193558 | The Oracle of Delphi | 0.409 | no |
24 | 2016 | 193739 | Jórvík | 0.401 | no |
25 | 2017 | 174430 | Gloomhaven | 0.395 | no |
26 | 2015 | 175878 | 504 | 0.370 | no |
27 | 2019 | 259081 | Machi Koro Legacy | 0.365 | no |
28 | 2013 | 146278 | Tash-Kalar: Arena of Legends | 0.359 | no |
29 | 2007 | 31260 | Agricola | 0.354 | no |
30 | 2011 | 96848 | Mage Knight Board Game | 0.342 | no |
31 | 2017 | 230933 | Merlin | 0.331 | no |
32 | 2014 | 163412 | Patchwork | 0.330 | yes |
33 | 2000 | 478 | Citadels | 0.325 | no |
34 | 2008 | 38159 | Ultimate Werewolf: Ultimate Edition | 0.313 | no |
35 | 2014 | 154246 | La Isla | 0.313 | yes |
36 | 2005 | 16496 | Roma | 0.308 | no |
37 | 2014 | 152241 | Ultimate Werewolf | 0.304 | no |
38 | 2014 | 159675 | Fields of Arle | 0.302 | no |
39 | 2010 | 98778 | Hanabi | 0.294 | no |
40 | 2015 | 181304 | Mysterium | 0.284 | no |
41 | 2007 | 31481 | Galaxy Trucker | 0.277 | yes |
42 | 2012 | 104710 | Wiz-War (Eighth Edition) | 0.276 | no |
43 | 2010 | 65200 | Asteroyds | 0.270 | no |
44 | 2019 | 255293 | One Night Ultimate Super Villains | 0.270 | no |
45 | 1997 | 11 | Bohnanza | 0.257 | yes |
46 | 2010 | 65907 | Mystery Express | 0.256 | no |
47 | 2018 | 245934 | Carpe Diem | 0.255 | yes |
48 | 2009 | 56931 | Arena: Roma II | 0.252 | no |
49 | 2013 | 127024 | Room 25 | 0.252 | no |
50 | 2015 | 180956 | One Night Ultimate Vampire | 0.248 | no |
51 | 2009 | 45315 | Dungeon Lords | 0.246 | no |
52 | 2016 | 198773 | Codenames: Pictures | 0.242 | no |
53 | 2010 | 67148 | Ultimate Werewolf: Compact Edition | 0.232 | no |
54 | 2010 | 73439 | Troyes | 0.231 | no |
55 | 2019 | 265260 | Revolution of 1828 | 0.221 | yes |
56 | 2015 | 178900 | Codenames | 0.219 | yes |
57 | 2002 | 4390 | Carcassonne: Hunters and Gatherers | 0.219 | no |
58 | 2008 | 37046 | Ghost Stories | 0.210 | no |
59 | 2011 | 70919 | Takenoko | 0.208 | yes |
60 | 2014 | 132531 | Roll for the Galaxy | 0.208 | no |
61 | 2012 | 119890 | Agricola: All Creatures Big and Small | 0.207 | no |
62 | 2018 | 255692 | New Frontiers | 0.203 | no |
63 | 2011 | 102680 | Trajan | 0.201 | yes |
64 | 2018 | 244049 | Forum Trajanum | 0.201 | yes |
65 | 2018 | 258466 | The Great City of Rome | 0.198 | no |
66 | 2009 | 40831 | The Pillars of the Earth: Builders Duel | 0.196 | no |
67 | 2017 | 220775 | Codenames: Disney – Family Edition | 0.183 | no |
68 | 2016 | 205637 | Arkham Horror: The Card Game | 0.179 | no |
69 | 2018 | 256916 | Concordia Venus | 0.177 | no |
70 | 2008 | 34635 | Stone Age | 0.175 | no |
71 | 2007 | 27682 | Ultimate Werewolf: Whitebox Edition | 0.175 | no |
72 | 2009 | 58798 | Cardcassonne | 0.175 | no |
73 | 2004 | 2651 | Power Grid | 0.173 | no |
74 | 2016 | 205418 | Agricola: Family Edition | 0.171 | no |
75 | 2006 | 21654 | Iliad | 0.171 | no |
76 | 2013 | 127060 | Bora Bora | 0.165 | yes |
77 | 2015 | 176361 | One Night Revolution | 0.163 | no |
78 | 2008 | 37111 | Battlestar Galactica: The Board Game | 0.160 | no |
79 | 2010 | 77130 | Sid Meier's Civilization: The Board Game | 0.160 | no |
80 | 2018 | 260428 | Pandemic: Fall of Rome | 0.158 | no |
81 | 2001 | 1345 | Genoa | 0.156 | no |
82 | 2012 | 118418 | Divinare | 0.155 | no |
83 | 2016 | 205158 | Codenames: Deep Undercover | 0.149 | no |
84 | 2018 | 206715 | Ultimate Werewolf Legacy | 0.147 | no |
85 | 2018 | 245422 | Werewords Deluxe Edition | 0.146 | no |
86 | 2017 | 255356 | Werwölfe | 0.145 | no |
87 | 2010 | 66193 | It Happens.. | 0.145 | no |
88 | 2010 | 85105 | Travel Blog | 0.145 | no |
89 | 2016 | 205867 | Bohnanza: The Duel | 0.144 | no |
90 | 2002 | 3076 | Puerto Rico | 0.142 | yes |
91 | 2014 | 148228 | Splendor | 0.139 | no |
92 | 2017 | 199966 | Kingsburg (Second Edition) | 0.133 | no |
93 | 2000 | 986 | Babel | 0.132 | no |
94 | 2010 | 66362 | Glen More | 0.131 | no |
95 | 2000 | 554 | La Città | 0.131 | no |
96 | 2016 | 200680 | Agricola (Revised Edition) | 0.128 | no |
97 | 2014 | 147949 | One Night Ultimate Werewolf | 0.126 | yes |
98 | 2017 | 199383 | Calimala | 0.126 | no |
99 | 2011 | 95064 | Ascension: Return of the Fallen | 0.125 | no |
100 | 2010 | 66197 | Spiel mit Lukas: Dribbel-Fieber | 0.124 | no |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.91 |
Decision Tree | roc_auc | binary | 0.71 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think Jkvandelay is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2010 | 70512 | Luna | 0.850 | no |
2010 | 66505 | The Speicherstadt | 0.812 | no |
2006 | 19948 | Rum & Pirates | 0.744 | no |
2009 | 55670 | Macao | 0.722 | no |
2011 | 91873 | Strasbourg | 0.626 | no |
What games does the model think Jkvandelay is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2008 | 38340 | Scene It? Seinfeld | 0.001 | yes |
2010 | 48726 | Alien Frontiers | 0.001 | yes |
2011 | 102652 | Sentinels of the Multiverse | 0.001 | yes |
2018 | 246784 | Cryptid | 0.001 | yes |
2014 | 156689 | Legendary: A Marvel Deck Building Game – Villains | 0.001 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Suburbia | Amerigo | AquaSphere | 504 | Citadels | The Castles of Burgundy: The Dice Game | Rising Sun | Last Bastion |
2 | Wiz-War (Eighth Edition) | Glass Road | Patchwork | Mysterium | The Castles of Burgundy: The Card Game | Twilight Imperium: Fourth Edition | Carpe Diem | Machi Koro Legacy |
3 | Agricola: All Creatures Big and Small | Bruges | La Isla | One Night Ultimate Vampire | The Oracle of Delphi | Gloomhaven | New Frontiers | One Night Ultimate Super Villains |
4 | Divinare | Tash-Kalar: Arena of Legends | Ultimate Werewolf | Codenames | Jórvík | Merlin | Forum Trajanum | Revolution of 1828 |
5 | Archipelago | Room 25 | Fields of Arle | One Night Revolution | Codenames: Pictures | Codenames: Disney – Family Edition | The Great City of Rome | Era: Medieval Age |
6 | Space Cadets | Bora Bora | Roll for the Galaxy | One Night Ultimate Werewolf: Daybreak | Arkham Horror: The Card Game | Werwölfe | Concordia Venus | Western Empires |
7 | Love Letter | The Little Prince: Make Me a Planet | Splendor | Mombasa | Agricola: Family Edition | Kingsburg (Second Edition) | Pandemic: Fall of Rome | Victorian Masterminds |
8 | Serenissima (Second Edition) | Rialto | One Night Ultimate Werewolf | Cacao | Codenames: Deep Undercover | Calimala | Ultimate Werewolf Legacy | Robin of Locksley |
9 | Clash of Cultures | Patchistory | Akrotiri | Het Koninkrijk Dominion | Bohnanza: The Duel | Pandemic Legacy: Season 2 | Werewords Deluxe Edition | Silver |
10 | Tokaido | Cinque Terre | Five Tribes | Blood Rage | Agricola (Revised Edition) | Werewords | Gen7: A Crossroads Game | Aftermath |
11 | Libertalia | You Suck | Tiny Epic Kingdoms | Bastion | Star Wars: Rebellion | One Night Ultimate Alien | Fireball Island: The Curse of Vul-Kar | Hellenica: Story of Greece |
12 | Dixit Jinx | Concordia | Colt Express | Elysium | The Manhattan Project: Energy Empire | Pandemic: Rising Tide | One Week Ultimate Werewolf | The King's Dilemma |
13 | Terra Mystica | Cappuccino | Pictopia: Disney Edition | Lancaster: Big Box | New Angeles | Codenames: Duet | Black Mirror: NOSEDIVE | MegaCity: Oceania |
14 | Dixit: Journey | Warhammer: Diskwars | Sheriff of Nottingham | Salem 1692 | Forged in Steel | Spirit Island | Lords of Hellas | Chocolate Factory |
15 | Mutant Meeples | Terror in Meeple City | Illegal | Unusual Suspects | Pandemic: Iberia | Dinosaur Island | Arkham Horror (Third Edition) | Nova Luna |
16 | Keyflower | Spyrium | King of New York | The Builders: Antiquity | Pandemic: Reign of Cthulhu | Pericles: The Peloponnesian Wars | Underwater Cities | Yukon Airways |
17 | Kemet | Bruxelles 1893 | Castles of Mad King Ludwig | Star Realms: Colony Wars | Terraforming Mars | Codenames: Marvel | Cosmic Encounter: 42nd Anniversary Edition | Throne of Allegoria |
18 | Robinson Crusoe: Adventures on the Cursed Island | Russian Railroads | Dogs of War | Risk: Europe | Game of Thrones: The Iron Throne | Valletta | Coimbra | Black Angel |
19 | Il Vecchio | Piñata | Quilt Show | Through the Ages: A New Story of Civilization | Cottage Garden | Harvest Dice | Heroes of Terrinoth | Pandemic: Rapid Response |
20 | Open Sesame | Renaissance Man | Rattlebones | Karuba | Bloodborne: The Card Game | Breaking Bad: The Board Game | Book of Dragons | Carnival of Monsters |
21 | Android: Netrunner | Lewis & Clark: The Expedition | Praetor | Hengist | Junta: Las Cartas | Templars' Journey | Star Realms: Frontiers | Peloponnesian War |
22 | City of Horror | Impulse | La Granja | Metal Adventures | Captain Sonar | Majesty: For the Realm | History of the World | Draftosaurus |
23 | Bohn to Be Wild! | Karnickel | Spyfall | Mafia de Cuba | A Feast for Odin | Coal Country | Azul: Stained Glass of Sintra | Queenz: To Bee or Not to Bee |
24 | Ginkgopolis | The Builders: Middle Ages | Pandemic: Contagion | The Voyages of Marco Polo | Scythe | Gaia Project | Narcos: The Board Game | Unmatched: Battle of Legends, Volume One |
25 | Ascension: Immortal Heroes | BANG! The Dice Game | Gaïa | Mega Civilization | Black Orchestra | Legend of the Five Rings: The Card Game | Founders of Gloomhaven | Unmatched Game System |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
Jkvandelay | owned | validation | GLM | roc_auc | 0.975 |
Jkvandelay | owned | validation | Decision Tree | roc_auc | 0.607 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 300327 | The Castles of Tuscany | 0.768 | no |
2020 | 316377 | 7 Wonders (Second Edition) | 0.330 | no |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.261 | no |
2020 | 304420 | Bonfire | 0.234 | yes |
2020 | 300322 | Hallertau | 0.128 | yes |
2020 | 318983 | Faiyum | 0.104 | no |
2020 | 269810 | Nevada City | 0.098 | no |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.090 | no |
2020 | 304285 | Infinity Gauntlet: A Love Letter Game | 0.080 | no |
2020 | 308765 | Praga Caput Regni | 0.080 | no |
2020 | 256317 | Guild Master | 0.063 | no |
2020 | 302417 | Mia London and the Case of the 625 Scoundrels | 0.056 | no |
2020 | 301716 | Glasgow | 0.055 | no |
2020 | 301919 | Pandemic: Hot Zone – North America | 0.051 | no |
2020 | 292333 | Cowboys II: Cowboys & Indians Edition | 0.049 | no |
2020 | 184267 | On Mars | 0.040 | no |
2020 | 316554 | Dune: Imperium | 0.039 | no |
2020 | 298572 | Cosmic Encounter Duel | 0.035 | no |
2020 | 317985 | Beyond the Sun | 0.033 | no |
2020 | 302926 | Silver Coin | 0.032 | no |
2020 | 253506 | Versailles 1919 | 0.032 | no |
2020 | 282171 | Trial by Trolley | 0.030 | no |
2020 | 287033 | Gray Eminence | 0.027 | no |
2020 | 315060 | Unmatched: Buffy the Vampire Slayer | 0.027 | no |
2020 | 294484 | Unmatched: Cobble & Fog | 0.027 | no |
2020 | 309630 | Small World of Warcraft | 0.027 | no |
2020 | 318098 | Silver Dagger | 0.025 | no |
2020 | 299592 | Beez | 0.024 | no |
2020 | 246900 | Eclipse: Second Dawn for the Galaxy | 0.023 | no |
2020 | 284777 | Unmatched: Jurassic Park – InGen vs Raptors | 0.021 | no |
2020 | 325635 | Unmatched: Little Red Riding Hood vs. Beowulf | 0.021 | no |
2020 | 300877 | New York Zoo | 0.018 | yes |
2020 | 319966 | The King Is Dead: Second Edition | 0.018 | no |
2020 | 317105 | Tiny Epic Galaxies BLAST OFF! | 0.016 | no |
2020 | 294788 | Conqueror: Final Conquest | 0.016 | no |
2020 | 296892 | Sacred Rites | 0.015 | no |
2020 | 301767 | Mysterium Park | 0.015 | no |
2020 | 295905 | Cosmic Frog | 0.015 | no |
2020 | 310448 | Zombie Teenz Evolution | 0.014 | no |
2020 | 313817 | Hello Neighbor: The Secret Neighbor Party Game | 0.013 | no |
2020 | 291508 | Tiny Epic Dinosaurs | 0.013 | no |
2020 | 312965 | Hogs of War: The Miniatures Game | 0.013 | no |
2020 | 294216 | Musical Chairs | 0.012 | no |
2020 | 308652 | Age of Dogfights: WW1 | 0.012 | no |
2020 | 291874 | Dwergar | 0.012 | no |
2020 | 271524 | TIME Stories Revolution: A Midsummer Night | 0.012 | no |
2020 | 276386 | Caesar: Rome vs. Gaul | 0.012 | no |
2020 | 300369 | Boomerang: USA | 0.012 | no |
2020 | 245658 | Unicorn Fever | 0.012 | no |
2020 | 312267 | Star Wars: Unlock! | 0.011 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Published | ID | Name | Pr(Owned) | Owned |
2022 | 314580 | Hamburg | 0.198 | no |
2021 | 311920 | Ultimate Werewolf: Extreme | 0.127 | no |
2021 | 322195 | Kokopelli | 0.093 | no |
2022 | 326945 | Castles of Mad King Ludwig: Collector's Edition | 0.092 | no |
2021 | 285967 | Ankh: Gods of Egypt | 0.086 | no |
2022 | 331106 | The Witcher: Old World | 0.080 | no |
2021 | 342848 | World of Warcraft: Wrath of the Lich King | 0.068 | no |
2022 | 310873 | Carnegie | 0.055 | no |
2021 | 338980 | Eastern Empires | 0.055 | no |
2022 | 351605 | Bohnanza: 25th Anniversary Edition | 0.047 | no |
2021 | 340466 | Unfathomable | 0.043 | no |
2021 | 330608 | Cryo | 0.041 | no |
2021 | 260524 | Beyond Humanity: Colonies | 0.041 | no |
2021 | 301257 | Maglev Metro | 0.037 | no |
2022 | 349793 | Age of Rome | 0.037 | no |
2022 | 283137 | Human Punishment: The Beginning | 0.035 | no |
2021 | 329670 | Pandemic: Hot Zone – Europe | 0.034 | no |
2021 | 328286 | Mission ISS | 0.034 | no |
2021 | 316287 | Quest | 0.031 | no |
2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.030 | no |
2021 | 331635 | Kameloot | 0.029 | no |
2021 | 341048 | Free Ride | 0.029 | no |
2021 | 343905 | Boonlake | 0.029 | no |
2021 | 320136 | Naruto: Ninja Arena | 0.026 | no |
2023 | 347909 | Rogue Angels: Legacy of the Burning Suns | 0.024 | no |
2021 | 298102 | Roll Camera!: The Filmmaking Board Game | 0.023 | no |
2021 | 336794 | Galaxy Trucker | 0.022 | no |
2022 | 288080 | Dice Realms | 0.021 | no |
2021 | 262941 | Dominant Species: Marine | 0.021 | no |
2021 | 290236 | Canvas | 0.021 | no |
2022 | 271601 | Feed the Kraken | 0.021 | no |
2022 | 266018 | Trinidad | 0.020 | no |
2022 | 335764 | Unmatched: Battle of Legends, Volume Two | 0.020 | no |
2021 | 273330 | Bloodborne: The Board Game | 0.019 | no |
2021 | 287608 | Epic Card Game: Duels | 0.019 | no |
2022 | 317511 | Tindaya | 0.019 | no |
2021 | 343696 | Dune: Betrayal | 0.018 | no |
2021 | 238799 | Messina 1347 | 0.018 | no |
2021 | 330036 | Great Plains | 0.016 | no |
2022 | 275215 | Namiji | 0.016 | no |
2021 | 249277 | Brazil: Imperial | 0.016 | no |
2021 | 344408 | Full Throttle! | 0.016 | no |
2021 | 298383 | Golem | 0.016 | no |
2021 | 300323 | Conquest & Consequence | 0.016 | no |
2022 | 316090 | Vivid Memories | 0.015 | no |
2021 | 291859 | Riftforce | 0.015 | no |
2021 | 239175 | Shiver Me Timbers | 0.014 | no |
2021 | 256680 | Return to Dark Tower | 0.013 | no |
2021 | 314491 | Meadow | 0.013 | no |
2021 | 300148 | Spy Connection | 0.013 | no |
2021 | 291847 | Mantis Falls | 0.013 | no |
2022 | 295770 | Frosthaven | 0.013 | no |
2021 | 346703 | 7 Wonders: Architects | 0.013 | no |
2021 | 347304 | Time's Up!: Harry Potter | 0.013 | no |
2021 | 288385 | Masters of the Night | 0.012 | no |
2021 | 341918 | Tulpenfieber | 0.011 | no |
2022 | 322524 | Bardsung | 0.011 | no |
2022 | 275284 | Arkeis | 0.011 | no |
2021 | 319792 | Fly-A-Way | 0.010 | no |
2021 | 304985 | Dark Ages: Holy Roman Empire | 0.010 | no |
2021 | 295535 | Dark Ages: Heritage of Charlemagne | 0.010 | no |
2022 | 251661 | Oathsworn: Into the Deepwood | 0.010 | no |
2022 | 258779 | Planet Unknown | 0.010 | no |
2021 | 329450 | Equinox | 0.010 | no |
2021 | 340455 | King of the Valley | 0.010 | no |
2021 | 331685 | Hit the Silk! | 0.009 | no |
2021 | 334782 | Bayou Bash | 0.009 | no |
2021 | 293835 | Oltréé | 0.009 | no |
2021 | 341169 | Great Western Trail (Second Edition) | 0.009 | no |
2021 | 291572 | Oath: Chronicles of Empire and Exile | 0.009 | no |
2022 | 326175 | The Smoky Valley | 0.009 | no |
2021 | 281676 | Galactic Era | 0.009 | no |
2021 | 320446 | Corduba 27 a.C. | 0.009 | no |
2021 | 297531 | Watch | 0.009 | no |
2021 | 339789 | Welcome to the Moon | 0.009 | no |
2021 | 313730 | Harsh Shadows | 0.009 | no |
2021 | 309319 | Apogee | 0.009 | no |
2021 | 347137 | Chronicles of Avel | 0.008 | no |
2022 | 346199 | A Game of Thrones: B'Twixt | 0.008 | no |
2021 | 314088 | Agropolis | 0.008 | no |
2022 | 305462 | The Age of Atlantis | 0.008 | no |
2021 | 298378 | Maharaja | 0.008 | no |
2021 | 193727 | Absolute War! The Russian Front 1941-45 | 0.008 | no |
2021 | 303954 | Pax Viking | 0.008 | no |
2021 | 339906 | The Hunger | 0.008 | no |
2021 | 339790 | Cocktail | 0.008 | no |
2021 | 206509 | Bayonets & Tomahawks | 0.008 | no |
2021 | 259066 | Commands & Colors: Samurai Battles | 0.008 | no |
2022 | 305096 | Endless Winter: Paleoamericans | 0.008 | no |
2021 | 259962 | Stress Botics | 0.008 | no |
2022 | 344050 | Dubious | 0.008 | no |
2021 | 325348 | Successors (Fourth Edition) | 0.008 | no |
2021 | 341009 | Armonia | 0.008 | no |
2023 | 337627 | Voidfall | 0.008 | no |
2021 | 344114 | Bag of Chips | 0.008 | no |
2021 | 316786 | Tabannusi: Builders of Ur | 0.008 | no |
2021 | 281248 | Cape May | 0.007 | no |
2022 | 280726 | Legacies | 0.007 | no |
2021 | 258242 | Magnate: The First City | 0.007 | no |
2021 | 292899 | Tribune | 0.007 | no |